Overview

Dataset statistics

Number of variables43
Number of observations25191
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.3 MiB
Average record size in memory344.0 B

Variable types

Numeric28
Categorical14
Text1

Alerts

Num_outbound_cmds has constant value ""Constant
Is_hot_login has constant value ""Constant
Count is highly overall correlated with Diff_srv_rate and 11 other fieldsHigh correlation
Diff_srv_rate is highly overall correlated with Count and 11 other fieldsHigh correlation
Dst_bytes is highly overall correlated with Diff_srv_rate and 8 other fieldsHigh correlation
Dst_host_count is highly overall correlated with Count and 5 other fieldsHigh correlation
Dst_host_diff_srv_rate is highly overall correlated with Diff_srv_rate and 6 other fieldsHigh correlation
Dst_host_rerror_rate is highly overall correlated with Dst_host_srv_rerror_rate and 2 other fieldsHigh correlation
Dst_host_same_src_port_rate is highly overall correlated with Count and 3 other fieldsHigh correlation
Dst_host_same_srv_rate is highly overall correlated with Diff_srv_rate and 12 other fieldsHigh correlation
Dst_host_serror_rate is highly overall correlated with Count and 10 other fieldsHigh correlation
Dst_host_srv_count is highly overall correlated with Diff_srv_rate and 8 other fieldsHigh correlation
Dst_host_srv_diff_host_rate is highly overall correlated with Count and 3 other fieldsHigh correlation
Dst_host_srv_rerror_rate is highly overall correlated with Dst_host_rerror_rate and 2 other fieldsHigh correlation
Dst_host_srv_serror_rate is highly overall correlated with Count and 7 other fieldsHigh correlation
Flag is highly overall correlated with Logged_in and 6 other fieldsHigh correlation
Hot is highly overall correlated with Is_guest_loginHigh correlation
Is_guest_login is highly overall correlated with HotHigh correlation
Land is highly overall correlated with attack_typeHigh correlation
Logged_in is highly overall correlated with Count and 6 other fieldsHigh correlation
Num_access_files is highly overall correlated with Su_attemptedHigh correlation
Num_compromised is highly overall correlated with Root_shell and 1 other fieldsHigh correlation
Num_root is highly overall correlated with Root_shell and 1 other fieldsHigh correlation
Protocol_type is highly overall correlated with attack_typeHigh correlation
Rerror_rate is highly overall correlated with Dst_host_rerror_rate and 3 other fieldsHigh correlation
Root_shell is highly overall correlated with Num_compromised and 2 other fieldsHigh correlation
Same_srv_rate is highly overall correlated with Count and 14 other fieldsHigh correlation
Serror_rate is highly overall correlated with Count and 10 other fieldsHigh correlation
Src_bytes is highly overall correlated with Count and 12 other fieldsHigh correlation
Srv_count is highly overall correlated with CountHigh correlation
Srv_rerror_rate is highly overall correlated with Dst_host_rerror_rate and 3 other fieldsHigh correlation
Srv_serror_rate is highly overall correlated with Count and 9 other fieldsHigh correlation
Su_attempted is highly overall correlated with Num_access_files and 3 other fieldsHigh correlation
Wrong_fragment is highly overall correlated with attack_typeHigh correlation
attack_type is highly overall correlated with Land and 3 other fieldsHigh correlation
Flag is highly imbalanced (55.9%)Imbalance
Land is highly imbalanced (99.9%)Imbalance
Wrong_fragment is highly imbalanced (95.0%)Imbalance
Urgent is highly imbalanced (99.9%)Imbalance
Num_failed_logins is highly imbalanced (99.5%)Imbalance
Root_shell is highly imbalanced (98.3%)Imbalance
Su_attempted is highly imbalanced (99.3%)Imbalance
Num_shells is highly imbalanced (99.5%)Imbalance
Is_guest_login is highly imbalanced (92.5%)Imbalance
attack_type is highly imbalanced (59.4%)Imbalance
Src_bytes is highly skewed (γ1 = 157.5554145)Skewed
Dst_bytes is highly skewed (γ1 = 54.77648949)Skewed
Num_compromised is highly skewed (γ1 = 62.18985248)Skewed
Num_root is highly skewed (γ1 = 62.31982588)Skewed
Num_file_creations is highly skewed (γ1 = 52.14065242)Skewed
Num_access_files is highly skewed (γ1 = 41.75193449)Skewed
Duration has 23167 (92.0%) zerosZeros
Src_bytes has 9866 (39.2%) zerosZeros
Dst_bytes has 13573 (53.9%) zerosZeros
Hot has 24671 (97.9%) zerosZeros
Num_compromised has 24919 (98.9%) zerosZeros
Num_root has 25057 (99.5%) zerosZeros
Num_file_creations has 25125 (99.7%) zerosZeros
Num_access_files has 25112 (99.7%) zerosZeros
Serror_rate has 17328 (68.8%) zerosZeros
Srv_serror_rate has 17707 (70.3%) zerosZeros
Rerror_rate has 21984 (87.3%) zerosZeros
Srv_rerror_rate has 21958 (87.2%) zerosZeros
Same_srv_rate has 543 (2.2%) zerosZeros
Diff_srv_rate has 15244 (60.5%) zerosZeros
Srv_diff_host_rate has 19516 (77.5%) zerosZeros
Dst_host_same_srv_rate has 1379 (5.5%) zerosZeros
Dst_host_diff_srv_rate has 9343 (37.1%) zerosZeros
Dst_host_same_src_port_rate has 12673 (50.3%) zerosZeros
Dst_host_srv_diff_host_rate has 17386 (69.0%) zerosZeros
Dst_host_serror_rate has 16220 (64.4%) zerosZeros
Dst_host_srv_serror_rate has 17004 (67.5%) zerosZeros
Dst_host_rerror_rate has 20688 (82.1%) zerosZeros
Dst_host_srv_rerror_rate has 21348 (84.7%) zerosZeros

Reproduction

Analysis started2024-05-01 16:22:45.722655
Analysis finished2024-05-01 16:25:07.558528
Duration2 minutes and 21.84 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Duration
Real number (ℝ)

ZEROS 

Distinct758
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.06621
Minimum0
Maximum42862
Zeros23167
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:07.683523image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum42862
Range42862
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2686.6083
Coefficient of variation (CV)8.8066398
Kurtosis146.69503
Mean305.06621
Median Absolute Deviation (MAD)0
Skewness11.53241
Sum7684923
Variance7217864
MonotonicityNot monotonic
2024-05-01T21:55:07.855398image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23167
92.0%
1 374
 
1.5%
2 165
 
0.7%
3 102
 
0.4%
4 75
 
0.3%
5 62
 
0.2%
6 41
 
0.2%
27 40
 
0.2%
28 38
 
0.2%
7 26
 
0.1%
Other values (748) 1101
 
4.4%
ValueCountFrequency (%)
0 23167
92.0%
1 374
 
1.5%
2 165
 
0.7%
3 102
 
0.4%
4 75
 
0.3%
5 62
 
0.2%
6 41
 
0.2%
7 26
 
0.1%
8 19
 
0.1%
9 22
 
0.1%
ValueCountFrequency (%)
42862 1
< 0.1%
42658 1
< 0.1%
42636 1
< 0.1%
42470 1
< 0.1%
42260 1
< 0.1%
42021 1
< 0.1%
41802 1
< 0.1%
41561 1
< 0.1%
41541 1
< 0.1%
41476 1
< 0.1%

Protocol_type
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
tcp
20525 
udp
3011 
icmp
 
1655

Length

Max length4
Median length3
Mean length3.0656981
Min length3

Characters and Unicode

Total characters77228
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowudp
2nd rowtcp
3rd rowtcp
4th rowtcp
5th rowtcp

Common Values

ValueCountFrequency (%)
tcp 20525
81.5%
udp 3011
 
12.0%
icmp 1655
 
6.6%

Length

2024-05-01T21:55:08.011648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T21:55:08.153275image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
tcp 20525
81.5%
udp 3011
 
12.0%
icmp 1655
 
6.6%

Most occurring characters

ValueCountFrequency (%)
p 25191
32.6%
c 22180
28.7%
t 20525
26.6%
u 3011
 
3.9%
d 3011
 
3.9%
i 1655
 
2.1%
m 1655
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 77228
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 25191
32.6%
c 22180
28.7%
t 20525
26.6%
u 3011
 
3.9%
d 3011
 
3.9%
i 1655
 
2.1%
m 1655
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 77228
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 25191
32.6%
c 22180
28.7%
t 20525
26.6%
u 3011
 
3.9%
d 3011
 
3.9%
i 1655
 
2.1%
m 1655
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 25191
32.6%
c 22180
28.7%
t 20525
26.6%
u 3011
 
3.9%
d 3011
 
3.9%
i 1655
 
2.1%
m 1655
 
2.1%
Distinct66
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:08.341497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.4735818
Min length3

Characters and Unicode

Total characters137885
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowother
2nd rowprivate
3rd rowhttp
4th rowhttp
5th rowprivate
ValueCountFrequency (%)
http 8003
31.8%
private 4351
17.3%
domain_u 1820
 
7.2%
smtp 1449
 
5.8%
ftp_data 1395
 
5.5%
eco_i 909
 
3.6%
other 858
 
3.4%
ecr_i 613
 
2.4%
telnet 483
 
1.9%
finger 366
 
1.5%
Other values (56) 4944
19.6%
2024-05-01T21:55:08.810939image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 29047
21.1%
p 17577
12.7%
a 10317
 
7.5%
e 9860
 
7.2%
h 9852
 
7.1%
i 9744
 
7.1%
r 6989
 
5.1%
_ 5927
 
4.3%
o 4909
 
3.6%
n 4525
 
3.3%
Other values (29) 29138
21.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 130361
94.5%
Connector Punctuation 5927
 
4.3%
Decimal Number 1283
 
0.9%
Uppercase Letter 314
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 29047
22.3%
p 17577
13.5%
a 10317
 
7.9%
e 9860
 
7.6%
h 9852
 
7.6%
i 9744
 
7.5%
r 6989
 
5.4%
o 4909
 
3.8%
n 4525
 
3.5%
v 4458
 
3.4%
Other values (15) 23083
17.7%
Decimal Number
ValueCountFrequency (%)
4 364
28.4%
3 338
26.3%
0 174
13.6%
5 172
13.4%
9 172
13.4%
1 45
 
3.5%
2 17
 
1.3%
8 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
Z 172
54.8%
I 40
 
12.7%
R 40
 
12.7%
C 40
 
12.7%
X 22
 
7.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 130675
94.8%
Common 7210
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 29047
22.2%
p 17577
13.5%
a 10317
 
7.9%
e 9860
 
7.5%
h 9852
 
7.5%
i 9744
 
7.5%
r 6989
 
5.3%
o 4909
 
3.8%
n 4525
 
3.5%
v 4458
 
3.4%
Other values (20) 23397
17.9%
Common
ValueCountFrequency (%)
_ 5927
82.2%
4 364
 
5.0%
3 338
 
4.7%
0 174
 
2.4%
5 172
 
2.4%
9 172
 
2.4%
1 45
 
0.6%
2 17
 
0.2%
8 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 29047
21.1%
p 17577
12.7%
a 10317
 
7.5%
e 9860
 
7.2%
h 9852
 
7.1%
i 9744
 
7.1%
r 6989
 
5.1%
_ 5927
 
4.3%
o 4909
 
3.6%
n 4525
 
3.3%
Other values (29) 29138
21.1%

Flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
SF
14972 
S0
7009 
REJ
2216 
RSTR
 
497
RSTO
 
304
Other values (6)
 
193

Length

Max length6
Median length2
Mean length2.1550951
Min length2

Characters and Unicode

Total characters54289
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSF
2nd rowS0
3rd rowSF
4th rowSF
5th rowREJ

Common Values

ValueCountFrequency (%)
SF 14972
59.4%
S0 7009
27.8%
REJ 2216
 
8.8%
RSTR 497
 
2.0%
RSTO 304
 
1.2%
S1 88
 
0.3%
SH 43
 
0.2%
RSTOS0 21
 
0.1%
S2 21
 
0.1%
S3 15
 
0.1%

Length

2024-05-01T21:55:08.982770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sf 14972
59.4%
s0 7009
27.8%
rej 2216
 
8.8%
rstr 497
 
2.0%
rsto 304
 
1.2%
s1 88
 
0.3%
sh 43
 
0.2%
rstos0 21
 
0.1%
s2 21
 
0.1%
s3 15
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 22991
42.3%
F 14972
27.6%
0 7030
 
12.9%
R 3535
 
6.5%
E 2216
 
4.1%
J 2216
 
4.1%
T 827
 
1.5%
O 330
 
0.6%
1 88
 
0.2%
H 48
 
0.1%
Other values (2) 36
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 47135
86.8%
Decimal Number 7154
 
13.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 22991
48.8%
F 14972
31.8%
R 3535
 
7.5%
E 2216
 
4.7%
J 2216
 
4.7%
T 827
 
1.8%
O 330
 
0.7%
H 48
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 7030
98.3%
1 88
 
1.2%
2 21
 
0.3%
3 15
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 47135
86.8%
Common 7154
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 22991
48.8%
F 14972
31.8%
R 3535
 
7.5%
E 2216
 
4.7%
J 2216
 
4.7%
T 827
 
1.8%
O 330
 
0.7%
H 48
 
0.1%
Common
ValueCountFrequency (%)
0 7030
98.3%
1 88
 
1.2%
2 21
 
0.3%
3 15
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54289
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 22991
42.3%
F 14972
27.6%
0 7030
 
12.9%
R 3535
 
6.5%
E 2216
 
4.1%
J 2216
 
4.1%
T 827
 
1.5%
O 330
 
0.6%
1 88
 
0.2%
H 48
 
0.1%
Other values (2) 36
 
0.1%

Src_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1665
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24331.575
Minimum0
Maximum3.8170909 × 108
Zeros9866
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:09.170694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median44
Q3279
95-th percentile1486.5
Maximum3.8170909 × 108
Range3.8170909 × 108
Interquartile range (IQR)279

Descriptive statistics

Standard deviation2410853.2
Coefficient of variation (CV)99.083322
Kurtosis24943.624
Mean24331.575
Median Absolute Deviation (MAD)44
Skewness157.55541
Sum6.129367 × 108
Variance5.8122134 × 1012
MonotonicityNot monotonic
2024-05-01T21:55:09.358337image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9866
39.2%
8 738
 
2.9%
1 480
 
1.9%
44 467
 
1.9%
45 416
 
1.7%
1032 390
 
1.5%
46 284
 
1.1%
43 231
 
0.9%
147 210
 
0.8%
105 204
 
0.8%
Other values (1655) 11905
47.3%
ValueCountFrequency (%)
0 9866
39.2%
1 480
 
1.9%
4 1
 
< 0.1%
5 4
 
< 0.1%
6 32
 
0.1%
7 26
 
0.1%
8 738
 
2.9%
9 39
 
0.2%
10 32
 
0.1%
11 14
 
0.1%
ValueCountFrequency (%)
381709090 1
 
< 0.1%
7665876 1
 
< 0.1%
7248552 1
 
< 0.1%
5135678 3
 
< 0.1%
5133876 8
 
< 0.1%
5131424 2
 
< 0.1%
5097472 1
 
< 0.1%
2280318 1
 
< 0.1%
2194620 2
 
< 0.1%
2194619 44
0.2%

Dst_bytes
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct3922
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3491.9858
Minimum0
Maximum5151385
Zeros13573
Zeros (%)53.9%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:09.561865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3530.5
95-th percentile8314
Maximum5151385
Range5151385
Interquartile range (IQR)530.5

Descriptive statistics

Standard deviation88832.479
Coefficient of variation (CV)25.438958
Kurtosis3130.0483
Mean3491.9858
Median Absolute Deviation (MAD)0
Skewness54.776489
Sum87966614
Variance7.8912093 × 109
MonotonicityNot monotonic
2024-05-01T21:55:09.796899image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13573
53.9%
105 309
 
1.2%
8314 175
 
0.7%
44 115
 
0.5%
42 105
 
0.4%
330 105
 
0.4%
332 103
 
0.4%
331 97
 
0.4%
4 94
 
0.4%
329 88
 
0.3%
Other values (3912) 10427
41.4%
ValueCountFrequency (%)
0 13573
53.9%
1 6
 
< 0.1%
4 94
 
0.4%
15 7
 
< 0.1%
17 7
 
< 0.1%
18 3
 
< 0.1%
24 2
 
< 0.1%
26 3
 
< 0.1%
28 2
 
< 0.1%
29 8
 
< 0.1%
ValueCountFrequency (%)
5151385 1
< 0.1%
5150836 1
< 0.1%
5150772 1
< 0.1%
5150180 1
< 0.1%
5149533 1
< 0.1%
5131424 1
< 0.1%
5129964 1
< 0.1%
1639484 1
< 0.1%
1593580 1
< 0.1%
1437092 1
< 0.1%

Land
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25189 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25191
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25189
> 99.9%
1 2
 
< 0.1%

Length

2024-05-01T21:55:10.377063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T21:55:10.486438image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 25189
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 25189
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25191
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25189
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25189
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25189
> 99.9%
1 2
 
< 0.1%

Wrong_fragment
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
24967 
3
 
187
1
 
37

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25191
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24967
99.1%
3 187
 
0.7%
1 37
 
0.1%

Length

2024-05-01T21:55:10.611585image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T21:55:10.736546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 24967
99.1%
3 187
 
0.7%
1 37
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 24967
99.1%
3 187
 
0.7%
1 37
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25191
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24967
99.1%
3 187
 
0.7%
1 37
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24967
99.1%
3 187
 
0.7%
1 37
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24967
99.1%
3 187
 
0.7%
1 37
 
0.1%

Urgent
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25190 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25191
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25190
> 99.9%
1 1
 
< 0.1%

Length

2024-05-01T21:55:10.862042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T21:55:10.972163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 25190
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 25190
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25191
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25190
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25190
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25190
> 99.9%
1 1
 
< 0.1%

Hot
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19804692
Minimum0
Maximum77
Zeros24671
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:11.128413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum77
Range77
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1542442
Coefficient of variation (CV)10.877443
Kurtosis213.68931
Mean0.19804692
Median Absolute Deviation (MAD)0
Skewness13.589264
Sum4989
Variance4.6407679
MonotonicityNot monotonic
2024-05-01T21:55:11.284696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 24671
97.9%
2 200
 
0.8%
1 78
 
0.3%
30 55
 
0.2%
28 52
 
0.2%
4 37
 
0.1%
6 26
 
0.1%
5 17
 
0.1%
22 13
 
0.1%
24 9
 
< 0.1%
Other values (12) 33
 
0.1%
ValueCountFrequency (%)
0 24671
97.9%
1 78
 
0.3%
2 200
 
0.8%
3 7
 
< 0.1%
4 37
 
0.1%
5 17
 
0.1%
6 26
 
0.1%
7 2
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
77 1
 
< 0.1%
30 55
0.2%
28 52
0.2%
25 1
 
< 0.1%
24 9
 
< 0.1%
22 13
 
0.1%
20 1
 
< 0.1%
19 8
 
< 0.1%
18 6
 
< 0.1%
17 1
 
< 0.1%

Num_failed_logins
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25168 
1
 
19
2
 
2
3
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25191
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25168
99.9%
1 19
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Length

2024-05-01T21:55:11.441273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T21:55:11.566272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 25168
99.9%
1 19
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 25168
99.9%
1 19
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25191
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25168
99.9%
1 19
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25168
99.9%
1 19
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25168
99.9%
1 19
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%

Logged_in
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
15246 
1
9945 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25191
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 15246
60.5%
1 9945
39.5%

Length

2024-05-01T21:55:11.707444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T21:55:11.816900image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 15246
60.5%
1 9945
39.5%

Most occurring characters

ValueCountFrequency (%)
0 15246
60.5%
1 9945
39.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25191
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15246
60.5%
1 9945
39.5%

Most occurring scripts

ValueCountFrequency (%)
Common 25191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15246
60.5%
1 9945
39.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15246
60.5%
1 9945
39.5%

Num_compromised
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22785916
Minimum0
Maximum884
Zeros24919
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:11.941899image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum884
Range884
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.417559
Coefficient of variation (CV)45.719289
Kurtosis4313.6044
Mean0.22785916
Median Absolute Deviation (MAD)0
Skewness62.189852
Sum5740
Variance108.52553
MonotonicityNot monotonic
2024-05-01T21:55:12.098184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 24919
98.9%
1 194
 
0.8%
2 21
 
0.1%
4 13
 
0.1%
6 8
 
< 0.1%
3 7
 
< 0.1%
5 5
 
< 0.1%
7 2
 
< 0.1%
151 2
 
< 0.1%
12 2
 
< 0.1%
Other values (18) 18
 
0.1%
ValueCountFrequency (%)
0 24919
98.9%
1 194
 
0.8%
2 21
 
0.1%
3 7
 
< 0.1%
4 13
 
0.1%
5 5
 
< 0.1%
6 8
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
884 1
< 0.1%
789 1
< 0.1%
558 1
< 0.1%
520 1
< 0.1%
462 1
< 0.1%
457 1
< 0.1%
371 1
< 0.1%
217 1
< 0.1%
193 1
< 0.1%
157 1
< 0.1%

Root_shell
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25152 
1
 
39

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25191
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25152
99.8%
1 39
 
0.2%

Length

2024-05-01T21:55:12.270133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T21:55:12.380314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 25152
99.8%
1 39
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 25152
99.8%
1 39
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25191
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25152
99.8%
1 39
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 25191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25152
99.8%
1 39
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25152
99.8%
1 39
 
0.2%

Su_attempted
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25170 
2
 
13
1
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25191
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25170
99.9%
2 13
 
0.1%
1 8
 
< 0.1%

Length

2024-05-01T21:55:12.505313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T21:55:12.614688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 25170
99.9%
2 13
 
0.1%
1 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 25170
99.9%
2 13
 
0.1%
1 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25191
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25170
99.9%
2 13
 
0.1%
1 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25170
99.9%
2 13
 
0.1%
1 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25170
99.9%
2 13
 
0.1%
1 8
 
< 0.1%

Num_root
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24985114
Minimum0
Maximum975
Zeros25057
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:12.756237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum975
Range975
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.50107
Coefficient of variation (CV)46.031689
Kurtosis4315.596
Mean0.24985114
Median Absolute Deviation (MAD)0
Skewness62.319826
Sum6294
Variance132.27461
MonotonicityNot monotonic
2024-05-01T21:55:12.912661image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 25057
99.5%
1 47
 
0.2%
9 24
 
0.1%
6 23
 
0.1%
2 10
 
< 0.1%
5 6
 
< 0.1%
4 2
 
< 0.1%
3 2
 
< 0.1%
14 1
 
< 0.1%
100 1
 
< 0.1%
Other values (18) 18
 
0.1%
ValueCountFrequency (%)
0 25057
99.5%
1 47
 
0.2%
2 10
 
< 0.1%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 6
 
< 0.1%
6 23
 
0.1%
7 1
 
< 0.1%
9 24
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
975 1
< 0.1%
867 1
< 0.1%
629 1
< 0.1%
572 1
< 0.1%
512 1
< 0.1%
508 1
< 0.1%
417 1
< 0.1%
247 1
< 0.1%
191 1
< 0.1%
179 1
< 0.1%

Num_file_creations
Real number (ℝ)

SKEWED  ZEROS 

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.014727482
Minimum0
Maximum40
Zeros25125
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:13.053290image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5296128
Coefficient of variation (CV)35.960852
Kurtosis3158.0796
Mean0.014727482
Median Absolute Deviation (MAD)0
Skewness52.140652
Sum371
Variance0.28048972
MonotonicityNot monotonic
2024-05-01T21:55:13.209540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 25125
99.7%
1 37
 
0.1%
2 7
 
< 0.1%
4 3
 
< 0.1%
8 2
 
< 0.1%
18 2
 
< 0.1%
5 2
 
< 0.1%
21 1
 
< 0.1%
11 1
 
< 0.1%
20 1
 
< 0.1%
Other values (10) 10
 
< 0.1%
ValueCountFrequency (%)
0 25125
99.7%
1 37
 
0.1%
2 7
 
< 0.1%
3 1
 
< 0.1%
4 3
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
8 2
 
< 0.1%
11 1
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
40 1
< 0.1%
38 1
< 0.1%
29 1
< 0.1%
21 1
< 0.1%
20 1
< 0.1%
19 1
< 0.1%
18 2
< 0.1%
17 1
< 0.1%
15 1
< 0.1%
14 1
< 0.1%

Num_shells
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25182 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25191
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25182
> 99.9%
1 9
 
< 0.1%

Length

2024-05-01T21:55:13.366055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T21:55:13.491102image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 25182
> 99.9%
1 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 25182
> 99.9%
1 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25191
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25182
> 99.9%
1 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 25191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25182
> 99.9%
1 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25182
> 99.9%
1 9
 
< 0.1%

Num_access_files
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0043269422
Minimum0
Maximum8
Zeros25112
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:13.600433image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.098525929
Coefficient of variation (CV)22.770336
Kurtosis2499.8086
Mean0.0043269422
Median Absolute Deviation (MAD)0
Skewness41.751934
Sum109
Variance0.0097073586
MonotonicityNot monotonic
2024-05-01T21:55:13.741092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 25112
99.7%
1 65
 
0.3%
2 8
 
< 0.1%
3 2
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 25112
99.7%
1 65
 
0.3%
2 8
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
5 2
 
< 0.1%
4 1
 
< 0.1%
3 2
 
< 0.1%
2 8
 
< 0.1%
1 65
 
0.3%
0 25112
99.7%

Num_outbound_cmds
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25191 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25191
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25191
100.0%

Length

2024-05-01T21:55:13.881680image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T21:55:14.006718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 25191
100.0%

Most occurring characters

ValueCountFrequency (%)
0 25191
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25191
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25191
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25191
100.0%

Is_hot_login
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
25191 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25191
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 25191
100.0%

Length

2024-05-01T21:55:14.116055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T21:55:14.225550image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 25191
100.0%

Most occurring characters

ValueCountFrequency (%)
0 25191
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25191
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 25191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25191
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25191
100.0%

Is_guest_login
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
0
24961 
1
 
230

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25191
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24961
99.1%
1 230
 
0.9%

Length

2024-05-01T21:55:14.352699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T21:55:14.476128image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 24961
99.1%
1 230
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 24961
99.1%
1 230
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 25191
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24961
99.1%
1 230
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 25191
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24961
99.1%
1 230
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25191
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24961
99.1%
1 230
 
0.9%

Count
Real number (ℝ)

HIGH CORRELATION 

Distinct466
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.594458
Minimum1
Maximum511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:14.694846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median14
Q3144
95-th percentile286
Maximum511
Range510
Interquartile range (IQR)142

Descriptive statistics

Standard deviation114.67455
Coefficient of variation (CV)1.3555799
Kurtosis1.9778428
Mean84.594458
Median Absolute Deviation (MAD)13
Skewness1.5036782
Sum2131019
Variance13150.252
MonotonicityNot monotonic
2024-05-01T21:55:14.898233image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5519
21.9%
2 1933
 
7.7%
3 769
 
3.1%
4 696
 
2.8%
5 597
 
2.4%
6 477
 
1.9%
7 462
 
1.8%
8 404
 
1.6%
9 342
 
1.4%
11 317
 
1.3%
Other values (456) 13675
54.3%
ValueCountFrequency (%)
1 5519
21.9%
2 1933
 
7.7%
3 769
 
3.1%
4 696
 
2.8%
5 597
 
2.4%
6 477
 
1.9%
7 462
 
1.8%
8 404
 
1.6%
9 342
 
1.4%
10 311
 
1.2%
ValueCountFrequency (%)
511 293
1.2%
510 58
 
0.2%
509 49
 
0.2%
508 6
 
< 0.1%
507 1
 
< 0.1%
506 1
 
< 0.1%
502 1
 
< 0.1%
500 3
 
< 0.1%
497 1
 
< 0.1%
496 2
 
< 0.1%

Srv_count
Real number (ℝ)

HIGH CORRELATION 

Distinct414
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.699774
Minimum1
Maximum511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:15.086172image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median8
Q318
95-th percentile157
Maximum511
Range510
Interquartile range (IQR)16

Descriptive statistics

Standard deviation72.4695
Coefficient of variation (CV)2.6162488
Kurtosis24.395617
Mean27.699774
Median Absolute Deviation (MAD)7
Skewness4.7074242
Sum697785
Variance5251.8284
MonotonicityNot monotonic
2024-05-01T21:55:15.273672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5080
20.2%
2 2537
 
10.1%
3 1223
 
4.9%
4 1086
 
4.3%
5 913
 
3.6%
6 849
 
3.4%
7 800
 
3.2%
8 751
 
3.0%
9 718
 
2.9%
11 688
 
2.7%
Other values (404) 10546
41.9%
ValueCountFrequency (%)
1 5080
20.2%
2 2537
10.1%
3 1223
 
4.9%
4 1086
 
4.3%
5 913
 
3.6%
6 849
 
3.4%
7 800
 
3.2%
8 751
 
3.0%
9 718
 
2.9%
10 647
 
2.6%
ValueCountFrequency (%)
511 200
0.8%
510 36
 
0.1%
509 6
 
< 0.1%
508 2
 
< 0.1%
500 1
 
< 0.1%
497 1
 
< 0.1%
496 1
 
< 0.1%
492 2
 
< 0.1%
489 1
 
< 0.1%
488 1
 
< 0.1%

Serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28634909
Minimum0
Maximum1
Zeros17328
Zeros (%)68.8%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:15.461733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44731756
Coefficient of variation (CV)1.5621407
Kurtosis-1.0745068
Mean0.28634909
Median Absolute Deviation (MAD)0
Skewness0.95258525
Sum7213.42
Variance0.200093
MonotonicityNot monotonic
2024-05-01T21:55:15.649242image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17328
68.8%
1 6941
27.6%
0.5 122
 
0.5%
0.07 53
 
0.2%
0.06 50
 
0.2%
0.05 50
 
0.2%
0.33 48
 
0.2%
0.01 46
 
0.2%
0.08 46
 
0.2%
0.25 43
 
0.2%
Other values (60) 464
 
1.8%
ValueCountFrequency (%)
0 17328
68.8%
0.01 46
 
0.2%
0.02 16
 
0.1%
0.03 31
 
0.1%
0.04 29
 
0.1%
0.05 50
 
0.2%
0.06 50
 
0.2%
0.07 53
 
0.2%
0.08 46
 
0.2%
0.09 38
 
0.2%
ValueCountFrequency (%)
1 6941
27.6%
0.99 41
 
0.2%
0.98 12
 
< 0.1%
0.97 16
 
0.1%
0.96 7
 
< 0.1%
0.95 6
 
< 0.1%
0.94 2
 
< 0.1%
0.93 6
 
< 0.1%
0.92 2
 
< 0.1%
0.91 1
 
< 0.1%

Srv_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28377357
Minimum0
Maximum1
Zeros17707
Zeros (%)70.3%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:15.837412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44760422
Coefficient of variation (CV)1.5773288
Kurtosis-1.0580062
Mean0.28377357
Median Absolute Deviation (MAD)0
Skewness0.96343827
Sum7148.54
Variance0.20034954
MonotonicityNot monotonic
2024-05-01T21:55:16.087722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17707
70.3%
1 7003
 
27.8%
0.5 94
 
0.4%
0.33 51
 
0.2%
0.25 42
 
0.2%
0.2 32
 
0.1%
0.05 26
 
0.1%
0.17 22
 
0.1%
0.03 20
 
0.1%
0.04 20
 
0.1%
Other values (46) 174
 
0.7%
ValueCountFrequency (%)
0 17707
70.3%
0.01 1
 
< 0.1%
0.02 13
 
0.1%
0.03 20
 
0.1%
0.04 20
 
0.1%
0.05 26
 
0.1%
0.06 10
 
< 0.1%
0.07 16
 
0.1%
0.08 10
 
< 0.1%
0.09 10
 
< 0.1%
ValueCountFrequency (%)
1 7003
27.8%
0.95 9
 
< 0.1%
0.94 1
 
< 0.1%
0.93 1
 
< 0.1%
0.92 3
 
< 0.1%
0.91 3
 
< 0.1%
0.9 4
 
< 0.1%
0.89 3
 
< 0.1%
0.88 1
 
< 0.1%
0.86 1
 
< 0.1%

Rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11863483
Minimum0
Maximum1
Zeros21984
Zeros (%)87.3%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:16.290809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31875092
Coefficient of variation (CV)2.6868241
Kurtosis3.5468717
Mean0.11863483
Median Absolute Deviation (MAD)0
Skewness2.3462888
Sum2988.53
Variance0.10160215
MonotonicityNot monotonic
2024-05-01T21:55:16.478345image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21984
87.3%
1 2552
 
10.1%
0.9 43
 
0.2%
0.89 39
 
0.2%
0.91 38
 
0.2%
0.92 38
 
0.2%
0.95 37
 
0.1%
0.5 36
 
0.1%
0.93 34
 
0.1%
0.94 31
 
0.1%
Other values (62) 359
 
1.4%
ValueCountFrequency (%)
0 21984
87.3%
0.01 8
 
< 0.1%
0.02 15
 
0.1%
0.03 21
 
0.1%
0.04 9
 
< 0.1%
0.05 8
 
< 0.1%
0.06 2
 
< 0.1%
0.07 8
 
< 0.1%
0.08 5
 
< 0.1%
0.09 1
 
< 0.1%
ValueCountFrequency (%)
1 2552
10.1%
0.99 6
 
< 0.1%
0.98 2
 
< 0.1%
0.97 7
 
< 0.1%
0.96 11
 
< 0.1%
0.95 37
 
0.1%
0.94 31
 
0.1%
0.93 34
 
0.1%
0.92 38
 
0.2%
0.91 38
 
0.2%

Srv_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12026517
Minimum0
Maximum1
Zeros21958
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:16.665846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32234086
Coefficient of variation (CV)2.6802511
Kurtosis3.5135494
Mean0.12026517
Median Absolute Deviation (MAD)0
Skewness2.3407176
Sum3029.6
Variance0.10390363
MonotonicityNot monotonic
2024-05-01T21:55:16.869097image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 21958
87.2%
1 2937
 
11.7%
0.5 56
 
0.2%
0.33 32
 
0.1%
0.25 26
 
0.1%
0.17 17
 
0.1%
0.2 16
 
0.1%
0.04 14
 
0.1%
0.08 11
 
< 0.1%
0.67 11
 
< 0.1%
Other values (32) 113
 
0.4%
ValueCountFrequency (%)
0 21958
87.2%
0.02 9
 
< 0.1%
0.03 8
 
< 0.1%
0.04 14
 
0.1%
0.05 8
 
< 0.1%
0.06 10
 
< 0.1%
0.07 7
 
< 0.1%
0.08 11
 
< 0.1%
0.09 2
 
< 0.1%
0.1 8
 
< 0.1%
ValueCountFrequency (%)
1 2937
11.7%
0.85 1
 
< 0.1%
0.84 1
 
< 0.1%
0.83 3
 
< 0.1%
0.81 2
 
< 0.1%
0.8 3
 
< 0.1%
0.79 2
 
< 0.1%
0.76 1
 
< 0.1%
0.75 5
 
< 0.1%
0.74 1
 
< 0.1%

Same_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct97
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66054543
Minimum0
Maximum1
Zeros543
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:17.057249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.09
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.91

Descriptive statistics

Standard deviation0.4396409
Coefficient of variation (CV)0.66557254
Kurtosis-1.6118083
Mean0.66054543
Median Absolute Deviation (MAD)0
Skewness-0.5704245
Sum16639.8
Variance0.19328412
MonotonicityNot monotonic
2024-05-01T21:55:17.244958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15356
61.0%
0.01 827
 
3.3%
0.02 710
 
2.8%
0.06 681
 
2.7%
0.03 678
 
2.7%
0.07 671
 
2.7%
0.04 658
 
2.6%
0.08 601
 
2.4%
0.05 590
 
2.3%
0 543
 
2.2%
Other values (87) 3876
 
15.4%
ValueCountFrequency (%)
0 543
2.2%
0.01 827
3.3%
0.02 710
2.8%
0.03 678
2.7%
0.04 658
2.6%
0.05 590
2.3%
0.06 681
2.7%
0.07 671
2.7%
0.08 601
2.4%
0.09 399
1.6%
ValueCountFrequency (%)
1 15356
61.0%
0.99 147
 
0.6%
0.98 18
 
0.1%
0.97 9
 
< 0.1%
0.96 4
 
< 0.1%
0.95 3
 
< 0.1%
0.94 3
 
< 0.1%
0.93 9
 
< 0.1%
0.92 6
 
< 0.1%
0.91 2
 
< 0.1%

Diff_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.062365527
Minimum0
Maximum1
Zeros15244
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:17.448083image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile0.29
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.17855311
Coefficient of variation (CV)2.8630097
Kurtosis19.294919
Mean0.062365527
Median Absolute Deviation (MAD)0
Skewness4.4176536
Sum1571.05
Variance0.031881212
MonotonicityNot monotonic
2024-05-01T21:55:17.635675image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15244
60.5%
0.06 3861
 
15.3%
0.07 1947
 
7.7%
0.05 1350
 
5.4%
1 663
 
2.6%
0.08 374
 
1.5%
0.01 194
 
0.8%
0.04 130
 
0.5%
0.09 121
 
0.5%
0.5 116
 
0.5%
Other values (69) 1191
 
4.7%
ValueCountFrequency (%)
0 15244
60.5%
0.01 194
 
0.8%
0.02 49
 
0.2%
0.03 48
 
0.2%
0.04 130
 
0.5%
0.05 1350
 
5.4%
0.06 3861
 
15.3%
0.07 1947
 
7.7%
0.08 374
 
1.5%
0.09 121
 
0.5%
ValueCountFrequency (%)
1 663
2.6%
0.99 10
 
< 0.1%
0.98 2
 
< 0.1%
0.97 1
 
< 0.1%
0.96 8
 
< 0.1%
0.95 12
 
< 0.1%
0.83 2
 
< 0.1%
0.82 1
 
< 0.1%
0.8 2
 
< 0.1%
0.79 1
 
< 0.1%

Srv_diff_host_rate
Real number (ℝ)

ZEROS 

Distinct57
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.095934659
Minimum0
Maximum1
Zeros19516
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:17.823186image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.25658723
Coefficient of variation (CV)2.674604
Kurtosis7.0096997
Mean0.095934659
Median Absolute Deviation (MAD)0
Skewness2.8858664
Sum2416.69
Variance0.065837005
MonotonicityNot monotonic
2024-05-01T21:55:17.995061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19516
77.5%
1 1559
 
6.2%
0.01 586
 
2.3%
0.67 210
 
0.8%
0.5 193
 
0.8%
0.12 170
 
0.7%
0.33 167
 
0.7%
0.25 164
 
0.7%
0.02 153
 
0.6%
0.11 143
 
0.6%
Other values (47) 2330
 
9.2%
ValueCountFrequency (%)
0 19516
77.5%
0.01 586
 
2.3%
0.02 153
 
0.6%
0.03 40
 
0.2%
0.04 39
 
0.2%
0.05 59
 
0.2%
0.06 118
 
0.5%
0.07 111
 
0.4%
0.08 124
 
0.5%
0.09 112
 
0.4%
ValueCountFrequency (%)
1 1559
6.2%
0.88 1
 
< 0.1%
0.83 1
 
< 0.1%
0.8 14
 
0.1%
0.75 58
 
0.2%
0.71 3
 
< 0.1%
0.67 210
 
0.8%
0.62 3
 
< 0.1%
0.6 41
 
0.2%
0.57 7
 
< 0.1%

Dst_host_count
Real number (ℝ)

HIGH CORRELATION 

Distinct256
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.53337
Minimum0
Maximum255
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:18.183093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q184
median255
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)171

Descriptive statistics

Standard deviation98.995648
Coefficient of variation (CV)0.54234275
Kurtosis-1.0447983
Mean182.53337
Median Absolute Deviation (MAD)0
Skewness-0.84318723
Sum4598198
Variance9800.1383
MonotonicityNot monotonic
2024-05-01T21:55:18.370593image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 14850
58.9%
1 601
 
2.4%
2 554
 
2.2%
3 251
 
1.0%
4 241
 
1.0%
5 162
 
0.6%
6 157
 
0.6%
8 134
 
0.5%
10 112
 
0.4%
11 111
 
0.4%
Other values (246) 8018
31.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 601
2.4%
2 554
2.2%
3 251
1.0%
4 241
1.0%
5 162
 
0.6%
6 157
 
0.6%
7 107
 
0.4%
8 134
 
0.5%
9 110
 
0.4%
ValueCountFrequency (%)
255 14850
58.9%
254 17
 
0.1%
253 17
 
0.1%
252 17
 
0.1%
251 12
 
< 0.1%
250 18
 
0.1%
249 15
 
0.1%
248 17
 
0.1%
247 21
 
0.1%
246 23
 
0.1%

Dst_host_srv_count
Real number (ℝ)

HIGH CORRELATION 

Distinct256
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.06661
Minimum0
Maximum255
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:18.543057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median61
Q3255
95-th percentile255
Maximum255
Range255
Interquartile range (IQR)245

Descriptive statistics

Standard deviation110.64759
Coefficient of variation (CV)0.96159599
Kurtosis-1.7510793
Mean115.06661
Median Absolute Deviation (MAD)59
Skewness0.294236
Sum2898643
Variance12242.889
MonotonicityNot monotonic
2024-05-01T21:55:18.777434image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 7148
28.4%
1 1658
 
6.6%
2 1041
 
4.1%
3 556
 
2.2%
4 516
 
2.0%
20 475
 
1.9%
5 464
 
1.8%
6 453
 
1.8%
254 440
 
1.7%
19 433
 
1.7%
Other values (246) 12007
47.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1658
6.6%
2 1041
4.1%
3 556
 
2.2%
4 516
 
2.0%
5 464
 
1.8%
6 453
 
1.8%
7 410
 
1.6%
8 426
 
1.7%
9 404
 
1.6%
ValueCountFrequency (%)
255 7148
28.4%
254 440
 
1.7%
253 91
 
0.4%
252 35
 
0.1%
251 81
 
0.3%
250 67
 
0.3%
249 42
 
0.2%
248 44
 
0.2%
247 57
 
0.2%
246 52
 
0.2%

Dst_host_same_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51980469
Minimum0
Maximum1
Zeros1379
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:19.029464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05
median0.51
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.95

Descriptive statistics

Standard deviation0.44894742
Coefficient of variation (CV)0.86368481
Kurtosis-1.8846373
Mean0.51980469
Median Absolute Deviation (MAD)0.49
Skewness-0.0040977447
Sum13094.4
Variance0.20155378
MonotonicityNot monotonic
2024-05-01T21:55:19.216037image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9758
38.7%
0.01 1541
 
6.1%
0 1379
 
5.5%
0.02 1325
 
5.3%
0.07 1122
 
4.5%
0.04 1046
 
4.2%
0.05 1017
 
4.0%
0.03 799
 
3.2%
0.06 701
 
2.8%
0.08 577
 
2.3%
Other values (91) 5926
23.5%
ValueCountFrequency (%)
0 1379
5.5%
0.01 1541
6.1%
0.02 1325
5.3%
0.03 799
3.2%
0.04 1046
4.2%
0.05 1017
4.0%
0.06 701
2.8%
0.07 1122
4.5%
0.08 577
 
2.3%
0.09 360
 
1.4%
ValueCountFrequency (%)
1 9758
38.7%
0.99 121
 
0.5%
0.98 170
 
0.7%
0.97 101
 
0.4%
0.96 160
 
0.6%
0.95 126
 
0.5%
0.94 87
 
0.3%
0.93 81
 
0.3%
0.92 72
 
0.3%
0.91 69
 
0.3%

Dst_host_diff_srv_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08254059
Minimum0
Maximum1
Zeros9343
Zeros (%)37.1%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:19.403538image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.03
Q30.07
95-th percentile0.56
Maximum1
Range1
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.18719454
Coefficient of variation (CV)2.2679089
Kurtosis12.726822
Mean0.08254059
Median Absolute Deviation (MAD)0.03
Skewness3.6160976
Sum2079.28
Variance0.035041794
MonotonicityNot monotonic
2024-05-01T21:55:19.591540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9343
37.1%
0.07 3448
 
13.7%
0.06 1917
 
7.6%
0.01 1881
 
7.5%
0.05 1436
 
5.7%
0.08 1367
 
5.4%
0.02 1327
 
5.3%
0.03 744
 
3.0%
0.04 603
 
2.4%
0.09 523
 
2.1%
Other values (91) 2602
 
10.3%
ValueCountFrequency (%)
0 9343
37.1%
0.01 1881
 
7.5%
0.02 1327
 
5.3%
0.03 744
 
3.0%
0.04 603
 
2.4%
0.05 1436
 
5.7%
0.06 1917
 
7.6%
0.07 3448
 
13.7%
0.08 1367
 
5.4%
0.09 523
 
2.1%
ValueCountFrequency (%)
1 408
1.6%
0.99 7
 
< 0.1%
0.98 6
 
< 0.1%
0.97 18
 
0.1%
0.96 12
 
< 0.1%
0.95 14
 
0.1%
0.94 12
 
< 0.1%
0.93 3
 
< 0.1%
0.92 6
 
< 0.1%
0.91 28
 
0.1%

Dst_host_same_src_port_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14745187
Minimum0
Maximum1
Zeros12673
Zeros (%)50.3%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:19.779003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.06
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.30837268
Coefficient of variation (CV)2.0913447
Kurtosis2.8105995
Mean0.14745187
Median Absolute Deviation (MAD)0
Skewness2.0984945
Sum3714.46
Variance0.095093709
MonotonicityNot monotonic
2024-05-01T21:55:19.967046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12673
50.3%
0.01 3557
 
14.1%
1 2052
 
8.1%
0.02 1115
 
4.4%
0.03 624
 
2.5%
0.04 447
 
1.8%
0.05 315
 
1.3%
0.5 232
 
0.9%
0.08 230
 
0.9%
0.06 226
 
0.9%
Other values (91) 3720
 
14.8%
ValueCountFrequency (%)
0 12673
50.3%
0.01 3557
 
14.1%
0.02 1115
 
4.4%
0.03 624
 
2.5%
0.04 447
 
1.8%
0.05 315
 
1.3%
0.06 226
 
0.9%
0.07 199
 
0.8%
0.08 230
 
0.9%
0.09 150
 
0.6%
ValueCountFrequency (%)
1 2052
8.1%
0.99 19
 
0.1%
0.98 37
 
0.1%
0.97 32
 
0.1%
0.96 46
 
0.2%
0.95 61
 
0.2%
0.94 24
 
0.1%
0.93 33
 
0.1%
0.92 15
 
0.1%
0.91 29
 
0.1%

Dst_host_srv_diff_host_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0318455
Minimum0
Maximum1
Zeros17386
Zeros (%)69.0%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:20.217073image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02
95-th percentile0.18
Maximum1
Range1
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.11057698
Coefficient of variation (CV)3.4722953
Kurtosis36.897522
Mean0.0318455
Median Absolute Deviation (MAD)0
Skewness5.6169481
Sum802.22
Variance0.012227269
MonotonicityNot monotonic
2024-05-01T21:55:20.420554image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17386
69.0%
0.02 1612
 
6.4%
0.01 1468
 
5.8%
0.03 950
 
3.8%
0.04 870
 
3.5%
0.05 608
 
2.4%
0.5 317
 
1.3%
0.06 264
 
1.0%
0.07 215
 
0.9%
0.25 205
 
0.8%
Other values (53) 1296
 
5.1%
ValueCountFrequency (%)
0 17386
69.0%
0.01 1468
 
5.8%
0.02 1612
 
6.4%
0.03 950
 
3.8%
0.04 870
 
3.5%
0.05 608
 
2.4%
0.06 264
 
1.0%
0.07 215
 
0.9%
0.08 87
 
0.3%
0.09 71
 
0.3%
ValueCountFrequency (%)
1 132
0.5%
0.97 1
 
< 0.1%
0.86 1
 
< 0.1%
0.8 1
 
< 0.1%
0.75 3
 
< 0.1%
0.67 16
 
0.1%
0.6 5
 
< 0.1%
0.57 5
 
< 0.1%
0.56 8
 
< 0.1%
0.55 4
 
< 0.1%

Dst_host_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2858116
Minimum0
Maximum1
Zeros16220
Zeros (%)64.4%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:20.608103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.44532167
Coefficient of variation (CV)1.5580952
Kurtosis-1.0614769
Mean0.2858116
Median Absolute Deviation (MAD)0
Skewness0.95808576
Sum7199.88
Variance0.19831139
MonotonicityNot monotonic
2024-05-01T21:55:20.795565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16220
64.4%
1 6739
26.8%
0.01 680
 
2.7%
0.02 237
 
0.9%
0.03 149
 
0.6%
0.04 83
 
0.3%
0.09 79
 
0.3%
0.08 69
 
0.3%
0.05 64
 
0.3%
0.99 59
 
0.2%
Other values (90) 812
 
3.2%
ValueCountFrequency (%)
0 16220
64.4%
0.01 680
 
2.7%
0.02 237
 
0.9%
0.03 149
 
0.6%
0.04 83
 
0.3%
0.05 64
 
0.3%
0.06 34
 
0.1%
0.07 47
 
0.2%
0.08 69
 
0.3%
0.09 79
 
0.3%
ValueCountFrequency (%)
1 6739
26.8%
0.99 59
 
0.2%
0.98 36
 
0.1%
0.97 18
 
0.1%
0.96 20
 
0.1%
0.95 14
 
0.1%
0.94 21
 
0.1%
0.93 15
 
0.1%
0.92 11
 
< 0.1%
0.91 8
 
< 0.1%

Dst_host_srv_serror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27985749
Minimum0
Maximum1
Zeros17004
Zeros (%)67.5%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:20.983253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4460807
Coefficient of variation (CV)1.5939566
Kurtosis-1.0218436
Mean0.27985749
Median Absolute Deviation (MAD)0
Skewness0.98427735
Sum7049.89
Variance0.19898799
MonotonicityNot monotonic
2024-05-01T21:55:21.577552image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17004
67.5%
1 6862
27.2%
0.01 758
 
3.0%
0.02 136
 
0.5%
0.03 32
 
0.1%
0.5 24
 
0.1%
0.08 16
 
0.1%
0.12 16
 
0.1%
0.04 15
 
0.1%
0.05 15
 
0.1%
Other values (78) 313
 
1.2%
ValueCountFrequency (%)
0 17004
67.5%
0.01 758
 
3.0%
0.02 136
 
0.5%
0.03 32
 
0.1%
0.04 15
 
0.1%
0.05 15
 
0.1%
0.06 15
 
0.1%
0.07 14
 
0.1%
0.08 16
 
0.1%
0.09 10
 
< 0.1%
ValueCountFrequency (%)
1 6862
27.2%
0.98 6
 
< 0.1%
0.97 13
 
0.1%
0.96 13
 
0.1%
0.95 6
 
< 0.1%
0.94 8
 
< 0.1%
0.93 7
 
< 0.1%
0.92 4
 
< 0.1%
0.91 9
 
< 0.1%
0.9 2
 
< 0.1%

Dst_host_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11780279
Minimum0
Maximum1
Zeros20688
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:21.765965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.30587502
Coefficient of variation (CV)2.5965007
Kurtosis3.7652704
Mean0.11780279
Median Absolute Deviation (MAD)0
Skewness2.3636408
Sum2967.57
Variance0.093559527
MonotonicityNot monotonic
2024-05-01T21:55:21.954003image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20688
82.1%
1 2069
 
8.2%
0.01 359
 
1.4%
0.02 232
 
0.9%
0.03 110
 
0.4%
0.05 84
 
0.3%
0.04 77
 
0.3%
0.92 52
 
0.2%
0.9 46
 
0.2%
0.91 43
 
0.2%
Other values (91) 1431
 
5.7%
ValueCountFrequency (%)
0 20688
82.1%
0.01 359
 
1.4%
0.02 232
 
0.9%
0.03 110
 
0.4%
0.04 77
 
0.3%
0.05 84
 
0.3%
0.06 33
 
0.1%
0.07 39
 
0.2%
0.08 37
 
0.1%
0.09 15
 
0.1%
ValueCountFrequency (%)
1 2069
8.2%
0.99 7
 
< 0.1%
0.98 12
 
< 0.1%
0.97 20
 
0.1%
0.96 39
 
0.2%
0.95 29
 
0.1%
0.94 23
 
0.1%
0.93 23
 
0.1%
0.92 52
 
0.2%
0.91 43
 
0.2%

Dst_host_srv_rerror_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct100
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11877417
Minimum0
Maximum1
Zeros21348
Zeros (%)84.7%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:22.188340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31733888
Coefficient of variation (CV)2.6717837
Kurtosis3.6327624
Mean0.11877417
Median Absolute Deviation (MAD)0
Skewness2.3604139
Sum2992.04
Variance0.10070397
MonotonicityNot monotonic
2024-05-01T21:55:22.392002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21348
84.7%
1 2617
 
10.4%
0.01 253
 
1.0%
0.02 124
 
0.5%
0.03 78
 
0.3%
0.04 69
 
0.3%
0.05 63
 
0.3%
0.06 36
 
0.1%
0.99 33
 
0.1%
0.98 30
 
0.1%
Other values (90) 540
 
2.1%
ValueCountFrequency (%)
0 21348
84.7%
0.01 253
 
1.0%
0.02 124
 
0.5%
0.03 78
 
0.3%
0.04 69
 
0.3%
0.05 63
 
0.3%
0.06 36
 
0.1%
0.07 17
 
0.1%
0.08 18
 
0.1%
0.09 11
 
< 0.1%
ValueCountFrequency (%)
1 2617
10.4%
0.99 33
 
0.1%
0.98 30
 
0.1%
0.97 19
 
0.1%
0.96 17
 
0.1%
0.95 11
 
< 0.1%
0.94 11
 
< 0.1%
0.93 10
 
< 0.1%
0.92 7
 
< 0.1%
0.91 7
 
< 0.1%

attack_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size196.9 KiB
normal
13448 
neptune
8282 
ipsweep
 
710
satan
 
691
portsweep
 
587
Other values (17)
1473 

Length

Max length15
Median length6
Mean length6.390973
Min length3

Characters and Unicode

Total characters160995
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rownormal
2nd rowneptune
3rd rownormal
4th rownormal
5th rowneptune

Common Values

ValueCountFrequency (%)
normal 13448
53.4%
neptune 8282
32.9%
ipsweep 710
 
2.8%
satan 691
 
2.7%
portsweep 587
 
2.3%
smurf 529
 
2.1%
nmap 301
 
1.2%
back 196
 
0.8%
teardrop 188
 
0.7%
warezclient 181
 
0.7%
Other values (12) 78
 
0.3%

Length

2024-05-01T21:55:22.595103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
normal 13448
53.4%
neptune 8282
32.9%
ipsweep 710
 
2.8%
satan 691
 
2.7%
portsweep 587
 
2.3%
smurf 529
 
2.1%
nmap 301
 
1.2%
back 196
 
0.8%
teardrop 188
 
0.7%
warezclient 181
 
0.7%
Other values (12) 78
 
0.3%

Most occurring characters

ValueCountFrequency (%)
n 31186
19.4%
e 19746
12.3%
a 15727
9.8%
r 15152
9.4%
m 14293
8.9%
o 14285
8.9%
l 13640
8.5%
p 11424
 
7.1%
t 9948
 
6.2%
u 8830
 
5.5%
Other values (14) 6764
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 160978
> 99.9%
Connector Punctuation 17
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 31186
19.4%
e 19746
12.3%
a 15727
9.8%
r 15152
9.4%
m 14293
8.9%
o 14285
8.9%
l 13640
8.5%
p 11424
 
7.1%
t 9948
 
6.2%
u 8830
 
5.5%
Other values (13) 6747
 
4.2%
Connector Punctuation
ValueCountFrequency (%)
_ 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 160978
> 99.9%
Common 17
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 31186
19.4%
e 19746
12.3%
a 15727
9.8%
r 15152
9.4%
m 14293
8.9%
o 14285
8.9%
l 13640
8.5%
p 11424
 
7.1%
t 9948
 
6.2%
u 8830
 
5.5%
Other values (13) 6747
 
4.2%
Common
ValueCountFrequency (%)
_ 17
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 31186
19.4%
e 19746
12.3%
a 15727
9.8%
r 15152
9.4%
m 14293
8.9%
o 14285
8.9%
l 13640
8.5%
p 11424
 
7.1%
t 9948
 
6.2%
u 8830
 
5.5%
Other values (14) 6764
 
4.2%

difficulty_level
Real number (ℝ)

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.487674
Minimum0
Maximum21
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size196.9 KiB
2024-05-01T21:55:22.737340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q118
median20
Q321
95-th percentile21
Maximum21
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3285848
Coefficient of variation (CV)0.11949014
Kurtosis13.164064
Mean19.487674
Median Absolute Deviation (MAD)1
Skewness-2.9001984
Sum490914
Variance5.4223074
MonotonicityNot monotonic
2024-05-01T21:55:22.891986image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
21 12495
49.6%
18 4142
 
16.4%
20 3861
 
15.3%
19 2047
 
8.1%
15 802
 
3.2%
17 587
 
2.3%
16 469
 
1.9%
12 162
 
0.6%
14 149
 
0.6%
11 126
 
0.5%
Other values (12) 351
 
1.4%
ValueCountFrequency (%)
0 12
 
< 0.1%
1 17
0.1%
2 10
 
< 0.1%
3 14
 
0.1%
4 14
 
0.1%
5 14
 
0.1%
6 27
0.1%
7 26
0.1%
8 25
0.1%
9 39
0.2%
ValueCountFrequency (%)
21 12495
49.6%
20 3861
 
15.3%
19 2047
 
8.1%
18 4142
 
16.4%
17 587
 
2.3%
16 469
 
1.9%
15 802
 
3.2%
14 149
 
0.6%
13 97
 
0.4%
12 162
 
0.6%

Interactions

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2024-05-01T21:54:44.478830image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:52.533756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:58.611350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:55:02.396904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:55:05.963237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:53:04.697423image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:53:08.544008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:53:12.082866image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:53:16.373804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:53:19.975234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:53:24.474126image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:53:32.946327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:53:40.356714image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:53:44.207295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:53:48.802987image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:53:52.599042image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:53:57.113755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:00.918965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:03.971214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:07.117923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:10.218256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:13.803871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:17.164192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:20.675451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:24.556475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:28.606069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:32.360595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:37.260600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:44.744967image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:52.818386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:54:58.830123image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-01T21:55:02.568783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-05-01T21:55:23.048241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
CountDiff_srv_rateDst_bytesDst_host_countDst_host_diff_srv_rateDst_host_rerror_rateDst_host_same_src_port_rateDst_host_same_srv_rateDst_host_serror_rateDst_host_srv_countDst_host_srv_diff_host_rateDst_host_srv_rerror_rateDst_host_srv_serror_rateDurationFlagHotIs_guest_loginLandLogged_inNum_access_filesNum_compromisedNum_failed_loginsNum_file_creationsNum_rootNum_shellsProtocol_typeRerror_rateRoot_shellSame_srv_rateSerror_rateSrc_bytesSrv_countSrv_diff_host_rateSrv_rerror_rateSrv_serror_rateSu_attemptedUrgentWrong_fragmentattack_typedifficulty_level
Count1.0000.615-0.4410.6180.3590.022-0.549-0.4300.537-0.325-0.5310.0220.507-0.3220.254-0.1500.0760.0000.649-0.054-0.0930.000-0.059-0.0840.0000.2780.0710.026-0.7200.579-0.5270.520-0.3210.0640.5430.0000.0000.0950.361-0.146
Diff_srv_rate0.6151.000-0.6040.5230.6450.156-0.447-0.7270.643-0.668-0.4790.1590.597-0.1350.145-0.1030.0080.0000.171-0.038-0.0770.000-0.026-0.0470.0000.1220.2320.000-0.9210.675-0.705-0.039-0.3770.2100.6250.0000.0000.0000.221-0.202
Dst_bytes-0.441-0.6041.000-0.340-0.624-0.2150.0610.667-0.5180.7030.325-0.181-0.4820.1490.0510.2000.0000.0000.0170.0730.1740.0000.048-0.0030.0000.000-0.2940.0000.628-0.5360.700-0.0200.313-0.271-0.5100.0000.0000.0000.3700.454
Dst_host_count0.6180.523-0.3401.0000.4350.053-0.696-0.5360.421-0.353-0.8350.0350.382-0.0590.161-0.0620.0720.0100.463-0.004-0.0460.027-0.022-0.0540.0000.2300.0860.036-0.5400.431-0.4080.219-0.3020.0790.4120.0220.0000.0470.231-0.121
Dst_host_diff_srv_rate0.3590.645-0.6240.4351.0000.269-0.212-0.8970.503-0.839-0.4870.2270.4360.1990.214-0.0610.0340.0000.174-0.002-0.0590.0280.0240.0350.0000.1720.2890.039-0.6490.487-0.526-0.242-0.4060.2730.4400.0340.0000.0690.273-0.260
Dst_host_rerror_rate0.0220.156-0.2150.0530.2691.0000.039-0.249-0.194-0.265-0.0670.884-0.2720.0690.3250.1020.0290.0000.277-0.0000.1280.0090.008-0.0040.0000.1200.8420.009-0.142-0.225-0.225-0.212-0.1420.833-0.2750.0130.0000.1400.231-0.190
Dst_host_same_src_port_rate-0.549-0.4470.061-0.696-0.2120.0391.0000.307-0.4590.1520.561-0.007-0.4540.1870.151-0.0060.0350.0200.209-0.012-0.0020.0000.0140.0570.0470.436-0.0120.0060.530-0.4880.381-0.1990.156-0.020-0.4740.0000.0000.1520.272-0.125
Dst_host_same_srv_rate-0.430-0.7270.667-0.536-0.897-0.2490.3071.000-0.5820.9180.538-0.222-0.517-0.1430.2660.0640.2440.0000.6320.0150.0630.026-0.018-0.0190.0450.224-0.2910.0350.757-0.5760.6190.2550.444-0.273-0.5300.0470.0000.1420.2990.269
Dst_host_serror_rate0.5370.643-0.5180.4210.503-0.194-0.459-0.5821.000-0.527-0.385-0.2080.919-0.1530.347-0.0640.0580.0000.497-0.027-0.0260.0520.000-0.0300.0340.213-0.1840.034-0.7170.934-0.6250.048-0.326-0.1900.9180.0350.0000.0540.318-0.176
Dst_host_srv_count-0.325-0.6680.703-0.353-0.839-0.2650.1520.918-0.5271.0000.451-0.249-0.471-0.1610.2520.0070.1690.0000.6510.0110.0010.014-0.029-0.0300.0290.259-0.3100.0000.697-0.5250.6190.3000.404-0.294-0.4790.0070.0000.1190.2960.381
Dst_host_srv_diff_host_rate-0.531-0.4790.325-0.835-0.487-0.0670.5610.538-0.3850.4511.000-0.045-0.343-0.0340.082-0.0680.0170.1030.144-0.012-0.0540.000-0.0040.0350.0000.440-0.0840.0410.487-0.3880.347-0.1250.333-0.083-0.3680.0470.0000.0330.3620.210
Dst_host_srv_rerror_rate0.0220.159-0.1810.0350.2270.884-0.007-0.222-0.208-0.249-0.0451.000-0.2500.0780.3610.1000.0590.0000.282-0.0010.1520.0720.006-0.0050.0000.1260.8850.071-0.155-0.208-0.255-0.219-0.1300.894-0.2600.1060.0000.0170.182-0.132
Dst_host_srv_serror_rate0.5070.597-0.4820.3820.436-0.272-0.454-0.5170.919-0.471-0.343-0.2501.000-0.1530.380-0.0710.0570.0900.495-0.028-0.0230.079-0.006-0.0200.0620.212-0.2280.065-0.6810.920-0.6100.078-0.305-0.2400.9400.0800.0000.0380.314-0.117
Duration-0.322-0.1350.149-0.0590.1990.0690.187-0.143-0.153-0.161-0.0340.078-0.1531.0000.1880.2250.0000.0000.0640.0480.1000.0300.0800.0540.0000.0810.0570.1290.167-0.1850.220-0.3210.0050.052-0.1830.1700.0000.0000.168-0.020
Flag0.2540.1450.0510.1610.2140.3250.1510.2660.3470.2520.0820.3610.3800.1881.0000.0880.0750.0000.6500.0390.0720.0690.0310.0540.0000.278-0.6300.0530.758-0.6050.8210.0810.372-0.617-0.5690.0660.0000.0520.4320.209
Hot-0.150-0.1030.200-0.062-0.0610.102-0.0060.064-0.0640.007-0.0680.100-0.0710.2250.0881.0000.8030.0000.0960.0170.4990.0000.0800.0160.1350.023-0.0060.0640.106-0.0880.202-0.141-0.0290.018-0.0830.0000.0000.0000.230-0.154
Is_guest_login0.0760.0080.0000.0720.0340.0290.0350.2440.0580.1690.0170.0590.0570.0000.0750.8031.0000.0000.1170.002-0.0100.0640.028-0.0070.0000.045-0.0340.0000.069-0.0640.128-0.132-0.045-0.034-0.0620.0000.0000.0020.291-0.060
Land0.0000.0000.0000.0100.0000.0000.0200.0000.0000.0000.1030.0000.0900.0000.0000.0000.0001.0000.000-0.000-0.0010.000-0.000-0.0010.0000.000-0.0030.0000.0070.014-0.010-0.0100.008-0.0030.0140.0000.0000.0000.707-0.016
Logged_in0.6490.1710.0170.4630.1740.2770.2090.6320.4970.6510.1440.2820.4950.0640.6500.0960.1170.0001.0000.0690.1290.0110.0620.0910.0200.385-0.2780.0470.596-0.4920.777-0.1360.266-0.256-0.4720.0350.0000.0760.7320.488
Num_access_files-0.054-0.0380.073-0.004-0.002-0.000-0.0120.015-0.0270.011-0.012-0.001-0.0280.0480.0390.0170.002-0.0000.0691.0000.0980.0000.0810.1140.1170.011-0.0150.4340.040-0.0340.064-0.0430.0210.007-0.0330.5030.0000.0000.1330.020
Num_compromised-0.093-0.0770.174-0.046-0.0590.128-0.0020.063-0.0260.001-0.0540.152-0.0230.1000.0720.499-0.010-0.0010.1290.0981.0000.0480.1540.2000.0000.000-0.0020.5230.078-0.0630.156-0.086-0.0150.029-0.0620.6800.0000.0000.000-0.150
Num_failed_logins0.0000.0000.0000.0270.0280.0090.0000.0260.0520.0140.0000.0720.0790.0300.0690.0000.0640.0000.0110.0000.0481.0000.1530.0520.0000.0000.0330.0000.023-0.0170.016-0.037-0.0160.032-0.0170.2460.0000.0000.362-0.046
Num_file_creations-0.059-0.0260.048-0.0220.0240.0080.014-0.0180.000-0.029-0.0040.006-0.0060.0800.0310.0800.028-0.0000.0620.0810.1540.1531.0000.1570.0000.000-0.0130.0910.031-0.0170.077-0.061-0.015-0.015-0.0200.2650.0000.0000.140-0.025
Num_root-0.084-0.047-0.003-0.0540.035-0.0040.057-0.019-0.030-0.0300.035-0.005-0.0200.0540.0540.016-0.007-0.0010.0910.1140.2000.0520.1571.0000.0000.000-0.0250.5220.051-0.0440.085-0.085-0.029-0.026-0.0420.7070.0000.0000.000-0.007
Num_shells0.0000.0000.0000.0000.0000.0000.0470.0450.0340.0290.0000.0000.0620.0000.0000.1350.0000.0000.0200.1170.0000.0000.0000.0001.0000.001-0.0070.0250.015-0.0130.0320.000-0.001-0.007-0.0120.0000.0000.0000.234-0.005
Protocol_type0.2780.1220.0000.2300.1720.1200.4360.2240.2130.2590.4400.1260.2120.0810.2780.0230.0450.0000.3850.0110.0000.0000.0000.0000.0011.000-0.0480.0170.034-0.087-0.0430.055-0.029-0.053-0.0890.0040.0000.1930.6630.061
Rerror_rate0.0710.232-0.2940.0860.2890.842-0.012-0.291-0.184-0.310-0.0840.885-0.2280.057-0.630-0.006-0.034-0.003-0.278-0.015-0.0020.033-0.013-0.025-0.007-0.0481.0000.016-0.219-0.176-0.356-0.204-0.1530.965-0.2250.0250.0000.0170.253-0.149
Root_shell0.0260.0000.0000.0360.0390.0090.0060.0350.0340.0000.0410.0710.0650.1290.0530.0640.0000.0000.0470.4340.5230.0000.0910.5220.0250.0170.0161.0000.028-0.0200.042-0.038-0.016-0.003-0.0190.5880.0800.0000.405-0.031
Same_srv_rate-0.720-0.9210.628-0.540-0.649-0.1420.5300.757-0.7170.6970.487-0.155-0.6810.1670.7580.1060.0690.0070.5960.0400.0780.0230.0310.0510.0150.034-0.2190.0281.000-0.7580.7540.0300.385-0.205-0.7080.0000.0000.0430.3240.171
Serror_rate0.5790.675-0.5360.4310.487-0.225-0.488-0.5760.934-0.525-0.388-0.2080.920-0.185-0.605-0.088-0.0640.014-0.492-0.034-0.063-0.017-0.017-0.044-0.013-0.087-0.176-0.020-0.7581.000-0.6750.074-0.324-0.1810.9740.0000.0000.1300.312-0.161
Src_bytes-0.527-0.7050.700-0.408-0.526-0.2250.3810.619-0.6250.6190.347-0.255-0.6100.2200.8210.2020.128-0.0100.7770.0640.1560.0160.0770.0850.032-0.043-0.3560.0420.754-0.6751.000-0.0560.289-0.335-0.6540.0000.0000.0000.0290.276
Srv_count0.520-0.039-0.0200.219-0.242-0.212-0.1990.2550.0480.300-0.125-0.2190.078-0.3210.081-0.141-0.132-0.010-0.136-0.043-0.086-0.037-0.061-0.0850.0000.055-0.204-0.0380.0300.074-0.0561.0000.235-0.2040.1090.0000.0000.2630.303-0.045
Srv_diff_host_rate-0.321-0.3770.313-0.302-0.406-0.1420.1560.444-0.3260.4040.333-0.130-0.3050.0050.372-0.029-0.0450.0080.2660.021-0.015-0.016-0.015-0.029-0.001-0.029-0.153-0.0160.385-0.3240.2890.2351.000-0.121-0.3060.0000.0000.0880.2120.138
Srv_rerror_rate0.0640.210-0.2710.0790.2730.833-0.020-0.273-0.190-0.294-0.0830.894-0.2400.052-0.6170.018-0.034-0.003-0.2560.0070.0290.032-0.015-0.026-0.007-0.0530.965-0.003-0.205-0.181-0.335-0.204-0.1211.000-0.2380.0000.0000.0170.220-0.138
Srv_serror_rate0.5430.625-0.5100.4120.440-0.275-0.474-0.5300.918-0.479-0.368-0.2600.940-0.183-0.569-0.083-0.0620.014-0.472-0.033-0.062-0.017-0.020-0.042-0.012-0.089-0.225-0.019-0.7080.974-0.6540.109-0.306-0.2381.0000.0000.0000.0390.328-0.137
Su_attempted0.0000.0000.0000.0220.0340.0130.0000.0470.0350.0070.0470.1060.0800.1700.0660.0000.0000.0000.0350.5030.6800.2460.2650.7070.0000.0040.0250.5880.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000-0.023
Urgent0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0800.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.499-0.012
Wrong_fragment0.0950.0000.0000.0470.0690.1400.1520.1420.0540.1190.0330.0170.0380.0000.0520.0000.0020.0000.0760.0000.0000.0000.0000.0000.0000.1930.0170.0000.0430.1300.0000.2630.0880.0170.0390.0000.0001.0000.979-0.150
attack_type0.3610.2210.3700.2310.2730.2310.2720.2990.3180.2960.3620.1820.3140.1680.4320.2300.2910.7070.7320.1330.0000.3620.1400.0000.2340.6630.2530.4050.3240.3120.0290.3030.2120.2200.3280.0000.4990.9791.0000.135
difficulty_level-0.146-0.2020.454-0.121-0.260-0.190-0.1250.269-0.1760.3810.210-0.132-0.117-0.0200.209-0.154-0.060-0.0160.4880.020-0.150-0.046-0.025-0.007-0.0050.061-0.149-0.0310.171-0.1610.276-0.0450.138-0.138-0.137-0.023-0.012-0.1500.1351.000

Missing values

2024-05-01T21:55:06.260767image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-01T21:55:07.089444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DurationProtocol_typeServiceFlagSrc_bytesDst_bytesLandWrong_fragmentUrgentHotNum_failed_loginsLogged_inNum_compromisedRoot_shellSu_attemptedNum_rootNum_file_creationsNum_shellsNum_access_filesNum_outbound_cmdsIs_hot_loginIs_guest_loginCountSrv_countSerror_rateSrv_serror_rateRerror_rateSrv_rerror_rateSame_srv_rateDiff_srv_rateSrv_diff_host_rateDst_host_countDst_host_srv_countDst_host_same_srv_rateDst_host_diff_srv_rateDst_host_same_src_port_rateDst_host_srv_diff_host_rateDst_host_serror_rateDst_host_srv_serror_rateDst_host_rerror_rateDst_host_srv_rerror_rateattack_typedifficulty_level
00udpotherSF146000000000000000001310.00.00.00.00.080.150.0025510.000.600.880.000.000.000.00.00normal15
10tcpprivateS000000000000000000012361.01.00.00.00.050.070.00255260.100.050.000.001.001.000.00.00neptune19
20tcphttpSF23281530000010000000000550.20.20.00.01.000.000.00302551.000.000.030.040.030.010.00.01normal21
30tcphttpSF199420000001000000000030320.00.00.00.01.000.000.092552551.000.000.000.000.000.000.00.00normal21
40tcpprivateREJ000000000000000000121190.00.01.01.00.160.060.00255190.070.070.000.000.000.001.01.00neptune21
50tcpprivateS000000000000000000016691.01.00.00.00.050.060.0025590.040.050.000.001.001.000.00.00neptune21
60tcpprivateS0000000000000000000117161.01.00.00.00.140.060.00255150.060.070.000.001.001.000.00.00neptune21
70tcpremote_jobS0000000000000000000270231.01.00.00.00.090.050.00255230.090.050.000.001.001.000.00.00neptune21
80tcpprivateS000000000000000000013381.01.00.00.00.060.060.00255130.050.060.000.001.001.000.00.00neptune21
90tcpprivateREJ000000000000000000205120.00.01.01.00.060.060.00255120.050.070.000.000.000.001.01.00neptune21
DurationProtocol_typeServiceFlagSrc_bytesDst_bytesLandWrong_fragmentUrgentHotNum_failed_loginsLogged_inNum_compromisedRoot_shellSu_attemptedNum_rootNum_file_creationsNum_shellsNum_access_filesNum_outbound_cmdsIs_hot_loginIs_guest_loginCountSrv_countSerror_rateSrv_serror_rateRerror_rateSrv_rerror_rateSame_srv_rateDiff_srv_rateSrv_diff_host_rateDst_host_countDst_host_srv_countDst_host_same_srv_rateDst_host_diff_srv_rateDst_host_same_src_port_rateDst_host_srv_diff_host_rateDst_host_serror_rateDst_host_srv_serror_rateDst_host_rerror_rateDst_host_srv_rerror_rateattack_typedifficulty_level
251810tcpotherREJ00000000000000000051110.120.000.851.00.001.000.0025510.001.000.000.000.160.00.821.0satan20
251820tcpprivateREJ00000000000000000031410.030.000.951.00.001.000.0025510.001.000.000.000.040.00.961.0satan18
2518329tcpftpSF32910630006010000000001110.000.000.000.01.000.000.00255600.240.020.000.000.000.00.030.1normal20
251841tcpsmtpSF28963330000010000000000130.000.000.000.01.000.001.0012110.920.170.080.000.000.00.000.0normal21
251850tcphttpS13391460000000100000000002330.500.030.000.01.000.000.061732551.000.000.010.010.010.00.010.0normal20
251860tcpexecRSTO00000000000000000010070.000.001.001.00.070.070.0025570.030.060.000.000.000.01.001.0neptune19
251870tcpftp_dataSF33400000010000000000110.000.000.000.01.000.000.001391.000.001.000.180.000.00.000.0warezclient12
251880tcpprivateREJ00000000000000000010570.000.001.001.00.070.070.00255130.050.070.000.000.000.01.001.0neptune21
251890tcpnnspS0000000000000000000129181.001.000.000.00.140.060.00255200.080.060.000.001.001.00.000.0neptune20
251900tcpfingerS00000000000000000003891.001.000.000.00.240.110.00255490.190.030.010.001.001.00.000.0neptune18